


'''simple docstring'''






import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np
import torch

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


__UpperCAmelCase       							=				logging.get_logger(__name__)




class 						a__							(				a__    ):





							'''simple docstring'''






							lowercase__     :  List[str]      		= ["input_features", "is_longer"]







							def __init__(						self   ,       lowerCamelCase_=64   ,       lowerCamelCase_=4_80_00   ,       lowerCamelCase_=4_80   ,       lowerCamelCase_=10   ,       lowerCamelCase_=10_24   ,       lowerCamelCase_=0.0   ,       lowerCamelCase_=False   ,       lowerCamelCase_ = 0   ,       lowerCamelCase_ = 1_40_00   ,       lowerCamelCase_ = None   ,       lowerCamelCase_ = "fusion"   ,       lowerCamelCase_ = "repeatpad"   ,       **lowerCamelCase_   ,       )			->				Tuple:
														super().__init__(
														    feature_size=lowerCamelCase_   ,       sampling_rate=lowerCamelCase_   ,       padding_value=lowerCamelCase_   ,       return_attention_mask=lowerCamelCase_   ,       **lowerCamelCase_   ,       )
														lowerCAmelCase__			  =      top_db
														lowerCAmelCase__			  =      truncation
														lowerCAmelCase__			  =      padding
														lowerCAmelCase__			  =      fft_window_size
														lowerCAmelCase__			  =      (fft_window_size >> 1) + 1
														lowerCAmelCase__			  =      hop_length
														lowerCAmelCase__			  =      max_length_s
														lowerCAmelCase__			  =      max_length_s * sampling_rate
														lowerCAmelCase__			  =      sampling_rate
														lowerCAmelCase__			  =      frequency_min
														lowerCAmelCase__			  =      frequency_max
														lowerCAmelCase__			  =      mel_filter_bank(
														    num_frequency_bins=self.nb_frequency_bins   ,       num_mel_filters=lowerCamelCase_   ,       min_frequency=lowerCamelCase_   ,       max_frequency=lowerCamelCase_   ,       sampling_rate=lowerCamelCase_   ,       norm=lowerCamelCase_   ,       mel_scale='''htk'''   ,       )
														lowerCAmelCase__			  =      mel_filter_bank(
														    num_frequency_bins=self.nb_frequency_bins   ,       num_mel_filters=lowerCamelCase_   ,       min_frequency=lowerCamelCase_   ,       max_frequency=lowerCamelCase_   ,       sampling_rate=lowerCamelCase_   ,       norm='''slaney'''   ,       mel_scale='''slaney'''   ,       )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Dict[str, Any]:
														lowerCAmelCase__			  =      copy.deepcopy(self.__dict__  )
														lowerCAmelCase__			  =      self.__class__.__name__
														if "mel_filters" in output:
																					del output["mel_filters"]
														if "mel_filters_slaney" in output:
																					del output["mel_filters_slaney"]
														return output







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_ = None  )			->				np.ndarray:
														lowerCAmelCase__			  =      spectrogram(
														    lowerCamelCase_   ,       window_function(self.fft_window_size   ,       '''hann'''  )   ,       frame_length=self.fft_window_size   ,       hop_length=self.hop_length   ,       power=2.0   ,       mel_filters=lowerCamelCase_   ,       log_mel='''dB'''   ,       )
														return log_mel_spectrogram.T







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_  )			->				str:
														lowerCAmelCase__			  =      np.array_split(list(range(0   ,       total_frames - chunk_frames + 1  )  )   ,       3  )
														if len(ranges[1]  ) == 0:
																					# if the audio is too short, we just use the first chunk
																					lowerCAmelCase__			  =      [0]
														if len(ranges[2]  ) == 0:
																					# if the audio is too short, we just use the first chunk
																					lowerCAmelCase__			  =      [0]
														# randomly choose index for each part
														lowerCAmelCase__			  =      np.random.choice(ranges[0]  )
														lowerCAmelCase__			  =      np.random.choice(ranges[1]  )
														lowerCAmelCase__			  =      np.random.choice(ranges[2]  )

														lowerCAmelCase__			  =      mel[idx_front : idx_front + chunk_frames, :]
														lowerCAmelCase__			  =      mel[idx_middle : idx_middle + chunk_frames, :]
														lowerCAmelCase__			  =      mel[idx_back : idx_back + chunk_frames, :]

														lowerCAmelCase__			  =      torch.tensor(mel[None, None, :]  )
														lowerCAmelCase__			  =      torch.nn.functional.interpolate(
														    lowerCamelCase_   ,       size=[chunk_frames, 64]   ,       mode='''bilinear'''   ,       align_corners=lowerCamelCase_  )
														lowerCAmelCase__			  =      mel_shrink[0][0].numpy()
														lowerCAmelCase__			  =      np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]   ,       axis=0  )
														return mel_fusion







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_  )			->				np.array:
														if waveform.shape[0] > max_length:
																					if truncation == "rand_trunc":
																												lowerCAmelCase__			  =      True
																												# random crop to max_length (for compatibility) -> this should be handled by self.pad
																												lowerCAmelCase__			  =      len(lowerCamelCase_  ) - max_length
																												lowerCAmelCase__			  =      np.random.randint(0   ,       overflow + 1  )
																												lowerCAmelCase__			  =      waveform[idx : idx + max_length]
																												lowerCAmelCase__			  =      self._np_extract_fbank_features(lowerCamelCase_   ,       self.mel_filters_slaney  )[None, :]
																					elif truncation == "fusion":
																												lowerCAmelCase__			  =      self._np_extract_fbank_features(lowerCamelCase_   ,       self.mel_filters  )
																												lowerCAmelCase__			  =      max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
																												lowerCAmelCase__			  =      mel.shape[0]
																												if chunk_frames == total_frames:
																																			# there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
																																			# In this case, we just use the whole audio.
																																			lowerCAmelCase__			  =      np.stack([mel, mel, mel, mel]   ,       axis=0  )
																																			lowerCAmelCase__			  =      False
																												else:
																																			lowerCAmelCase__			  =      self._random_mel_fusion(lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_  )
																																			lowerCAmelCase__			  =      True
																					else:
																												raise NotImplementedError(F"""data_truncating {truncation} not implemented"""  )

														else:
																					lowerCAmelCase__			  =      False
																					# only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
																					if waveform.shape[0] < max_length:
																												if padding == "repeat":
																																			lowerCAmelCase__			  =      int(max_length / len(lowerCamelCase_  )  )
																																			lowerCAmelCase__			  =      np.stack(np.tile(lowerCamelCase_   ,       n_repeat + 1  )  )[:max_length]
																												if padding == "repeatpad":
																																			lowerCAmelCase__			  =      int(max_length / len(lowerCamelCase_  )  )
																																			lowerCAmelCase__			  =      np.stack(np.tile(lowerCamelCase_   ,       lowerCamelCase_  )  )
																												lowerCAmelCase__			  =      np.pad(lowerCamelCase_   ,       (0, max_length - waveform.shape[0])   ,       mode='''constant'''   ,       constant_values=0  )

																					if truncation == "fusion":
																												lowerCAmelCase__			  =      self._np_extract_fbank_features(lowerCamelCase_   ,       self.mel_filters  )
																												lowerCAmelCase__			  =      np.stack([input_mel, input_mel, input_mel, input_mel]   ,       axis=0  )
																					else:
																												lowerCAmelCase__			  =      self._np_extract_fbank_features(lowerCamelCase_   ,       self.mel_filters_slaney  )[None, :]

														return input_mel, longer







							def __call__(						self   ,       lowerCamelCase_   ,       lowerCamelCase_ = None   ,       lowerCamelCase_ = None   ,       lowerCamelCase_ = None   ,       lowerCamelCase_ = None   ,       lowerCamelCase_ = None   ,       **lowerCamelCase_   ,       )			->				BatchFeature:
														lowerCAmelCase__			  =      truncation if truncation is not None else self.truncation
														lowerCAmelCase__			  =      padding if padding else self.padding

														if sampling_rate is not None:
																					if sampling_rate != self.sampling_rate:
																												raise ValueError(
																												    F"""The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a"""
																												    F""" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input"""
																												    F""" was sampled with {self.sampling_rate} and not {sampling_rate}."""  )
														else:
																					logger.warning(
																					    '''It is strongly recommended to pass the `sampling_rate` argument to this function. '''
																					    '''Failing to do so can result in silent errors that might be hard to debug.'''  )

														lowerCAmelCase__			  =      isinstance(lowerCamelCase_   ,       np.ndarray  ) and len(raw_speech.shape  ) > 1
														if is_batched_numpy and len(raw_speech.shape  ) > 2:
																					raise ValueError(F"""Only mono-channel audio is supported for input to {self}"""  )
														lowerCAmelCase__			  =      is_batched_numpy or (
														    isinstance(lowerCamelCase_   ,       (list, tuple)  ) and (isinstance(raw_speech[0]   ,       (np.ndarray, tuple, list)  ))
														)

														if is_batched:
																					lowerCAmelCase__			  =      [np.asarray(lowerCamelCase_   ,       dtype=np.floataa  ) for speech in raw_speech]
														elif not is_batched and not isinstance(lowerCamelCase_   ,       np.ndarray  ):
																					lowerCAmelCase__			  =      np.asarray(lowerCamelCase_   ,       dtype=np.floataa  )
														elif isinstance(lowerCamelCase_   ,       np.ndarray  ) and raw_speech.dtype is np.dtype(np.floataa  ):
																					lowerCAmelCase__			  =      raw_speech.astype(np.floataa  )

														# always return batch
														if not is_batched:
																					lowerCAmelCase__			  =      [np.asarray(lowerCamelCase_  )]

														# convert to mel spectrogram, truncate and pad if needed.
														lowerCAmelCase__			  =      [
														    self._get_input_mel(lowerCamelCase_   ,       max_length if max_length else self.nb_max_samples   ,       lowerCamelCase_   ,       lowerCamelCase_  )
														    for waveform in raw_speech
														]

														lowerCAmelCase__			  =      []
														lowerCAmelCase__			  =      []
														for mel, longer in padded_inputs:
																					input_mel.append(lowerCamelCase_  )
																					is_longer.append(lowerCamelCase_  )

														if truncation == "fusion" and sum(lowerCamelCase_  ) == 0:
																					# if no audio is longer than 10s, then randomly select one audio to be longer
																					lowerCAmelCase__			  =      np.random.randint(0   ,       len(lowerCamelCase_  )  )
																					lowerCAmelCase__			  =      True

														if isinstance(input_mel[0]   ,       lowerCamelCase_  ):
																					lowerCAmelCase__			  =      [np.asarray(lowerCamelCase_   ,       dtype=np.floataa  ) for feature in input_mel]

														# is_longer is a list of bool
														lowerCAmelCase__			  =      [[longer] for longer in is_longer]

														lowerCAmelCase__			  =      {'''input_features''': input_mel, '''is_longer''': is_longer}
														lowerCAmelCase__			  =      BatchFeature(lowerCamelCase_  )

														if return_tensors is not None:
																					lowerCAmelCase__			  =      input_features.convert_to_tensors(lowerCamelCase_  )

														return input_features

"""simple docstring"""


from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
      import datasets



class 	__a	(   _lowerCAmelCase						):






       @slow
       @require_torch
       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[int]      )->	Dict:







              """simple docstring"""
              UpperCamelCase       			=		EncoderDecoderModel.from_encoder_decoder_pretrained("prajjwal1/bert-tiny"							,   "prajjwal1/bert-tiny"      )
              UpperCamelCase       			=		BertTokenizer.from_pretrained("bert-base-uncased"      )

              UpperCamelCase       			=		bertabert.config.encoder.vocab_size
              UpperCamelCase       			=		tokenizer.sep_token_id
              UpperCamelCase       			=		tokenizer.cls_token_id
              UpperCamelCase       			=		128

              UpperCamelCase       			=		datasets.load_dataset("cnn_dailymail"							,   "3.0.0"							,   split="train[:1%]"      )
              UpperCamelCase       			=		datasets.load_dataset("cnn_dailymail"							,   "3.0.0"							,   split="validation[:1%]"      )

              UpperCamelCase       			=		train_dataset.select(range(32      )      )
              UpperCamelCase       			=		val_dataset.select(range(16      )      )

              UpperCamelCase       			=		4

              def _map_to_encoder_decoder_inputs(UpperCAmelCase_			:				Any      ):
                     # Tokenizer will automatically set [BOS] <text> [EOS]
                     UpperCamelCase       			=		tokenizer(batch["article"]							,   padding="max_length"							,   truncation=UpperCAmelCase_							,   max_length=512      )
                     UpperCamelCase       			=		tokenizer(batch["highlights"]							,   padding="max_length"							,   truncation=UpperCAmelCase_							,   max_length=128      )
                     UpperCamelCase       			=		inputs.input_ids
                     UpperCamelCase       			=		inputs.attention_mask

                     UpperCamelCase       			=		outputs.input_ids
                     UpperCamelCase       			=		outputs.input_ids.copy()
                     UpperCamelCase       			=		[
                         [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["labels"]
                     ]
                     UpperCamelCase       			=		outputs.attention_mask

                     assert all(len(UpperCAmelCase_      ) == 512 for x in inputs.input_ids      )
                     assert all(len(UpperCAmelCase_      ) == 128 for x in outputs.input_ids      )

                     return batch

              def _compute_metrics(UpperCAmelCase_			:				Any      ):
                     UpperCamelCase       			=		pred.label_ids
                     UpperCamelCase       			=		pred.predictions

                     # all unnecessary tokens are removed
                     UpperCamelCase       			=		tokenizer.batch_decode(UpperCAmelCase_							,   skip_special_tokens=UpperCAmelCase_      )
                     UpperCamelCase       			=		tokenizer.batch_decode(UpperCAmelCase_							,   skip_special_tokens=UpperCAmelCase_      )

                     UpperCamelCase       			=		sum([int(pred_str[i] == label_str[i]      ) for i in range(len(UpperCAmelCase_      )      )]      ) / len(UpperCAmelCase_      )

                     return {"accuracy": accuracy}

              # map train dataset
              UpperCamelCase       			=		train_dataset.map(
                  _map_to_encoder_decoder_inputs							,   batched=UpperCAmelCase_							,   batch_size=UpperCAmelCase_							,   remove_columns=["article", "highlights"]							,   )
              train_dataset.set_format(
                  type="torch"							,   columns=["input_ids", "attention_mask", "decoder_input_ids", "decoder_attention_mask", "labels"]							,   )

              # same for validation dataset
              UpperCamelCase       			=		val_dataset.map(
                  _map_to_encoder_decoder_inputs							,   batched=UpperCAmelCase_							,   batch_size=UpperCAmelCase_							,   remove_columns=["article", "highlights"]							,   )
              val_dataset.set_format(
                  type="torch"							,   columns=["input_ids", "attention_mask", "decoder_input_ids", "decoder_attention_mask", "labels"]							,   )

              UpperCamelCase       			=		self.get_auto_remove_tmp_dir()

              UpperCamelCase       			=		SeqaSeqTrainingArguments(
                  output_dir=UpperCAmelCase_							,   per_device_train_batch_size=UpperCAmelCase_							,   per_device_eval_batch_size=UpperCAmelCase_							,   predict_with_generate=UpperCAmelCase_							,   evaluation_strategy="steps"							,   do_train=UpperCAmelCase_							,   do_eval=UpperCAmelCase_							,   warmup_steps=0							,   eval_steps=2							,   logging_steps=2							,   )

              # instantiate trainer
              UpperCamelCase       			=		SeqaSeqTrainer(
                  model=UpperCAmelCase_							,   args=UpperCAmelCase_							,   compute_metrics=_compute_metrics							,   train_dataset=UpperCAmelCase_							,   eval_dataset=UpperCAmelCase_							,   tokenizer=UpperCAmelCase_							,   )

              # start training
              trainer.train()





